Physiological activity is monitored in the diagnosis and treatment of numerous diseases and medical conditions. For example, heart activity is commonly monitored by collecting electrocardiographic (“ECG”) data. ECG data is typically interpreted by a cardiologist, a physician specially trained in reading the waveforms created by ECG equipment.
In many situations, ECG and other physiological data is available, but an expert suitably trained in reading that data is not. In response to this problem, software interpretation tools have been developed to aid the non-expert physician in interpreting and using such data. However, these tools, particularly ECG tools, are not satisfactory. Existing ECG tools are designed to generate an interpretation. The computer-generated interpretation may be supported by one or more statements that describe the criteria that the computer uses to reach its conclusion. However, these statements are typically limited to describing the character of the waveform, which is usually of little assistance to the novice ECG reader. Another shortcoming of existing tools is that they generate conclusions based on the assumption that the ECG device correctly measured the ECG. In other words, existing computer tools assume that no faults or other errors ever occur in ECG measuring equipment.
The output from an existing ECG interpretation system is shown in FIG. 1. The output includes a screen image 10. The image 10 includes patient identifying information 12, initial diagnostic information 14, such as a complaint or symptom, measurements 16, physiological data in the form of waveforms 18, a diagnosis or interpretation 22, and a group of reason statements 24. In the example shown, the interpretation 22 indicates that there is a 42% probability that the patient has acute cardiac ischemia. The reasons supporting the interpretation are set out in the reason statements 24. For example, the interpretation is based on the fact that the patient is male, complaining of chest pain, and that “no significant Q waves or primary ST segment abnormalities” were detected. The reason statements 24 also indicate that the “[a]nterior T waves” are flat.
Most non-specialists find reason statements such as those shown in FIG. 1 to be too technical and, therefore, unhelpful in understanding the interpretation. Further, non-specialists are generally uncomfortable relying on an interpretation lacking a high probability. In the example shown, the interpretation generated has a probability of only 42%, meaning that there is a 58% chance that another interpretation is appropriate for the data. Thus, in those cases where present systems generate an interpretation of equivocal probability, they are often of little help.
Moreover, in cases where a patient is about to undergo a non-cardiac surgery, there is a need to be able to accurately assess the perioperative cardiovascular risk to the patient of performing the surgery. Again, having to wait for a cardiologist to make such an assessment may not be practical or even possible.